Cloud that is sized for the traffic you actually have, described entirely in code, and cheap enough that nobody dreads the invoice. Most of the estates we inherit are not badly built; they were sized for a launch that never arrived and never revisited afterwards. We map what runs, what it costs and what breaks it, then automate the release path until deploying is the least interesting thing that happens in a week. Everything lives in your own tenancy, so there is nothing to migrate when we are done.
The same four moves on every engagement of this kind. No discovery phase that bills for months before anything runs.
We map what you run now, what it costs and where it breaks. Nothing changes in this step. The point is a shared and accurate picture.
Infrastructure moves into version-controlled Terraform, so environments become reproducible and a change becomes a reviewable pull request.
Workloads move one at a time behind a rollback, never in a single cutover weekend. Each move is proven before the next one starts.
Runbooks, alerts that page for real problems only, and a cost baseline your team can act on. The aim is to make ourselves unnecessary here.
Right-sized for real traffic rather than the traffic in the pitch deck. Most cloud bills are not a pricing problem; they are an architecture that was sized for a launch that never came, and never revisited.
Build, test, scan and deploy, automated end to end, with the rollback path tested rather than assumed. Releasing should be the least interesting thing that happens in a week.
Every environment described in version control, reviewable in a pull request, reproducible from scratch. Anything clicked into a console once is a thing nobody can rebuild at three in the morning.
Tagging, budgets and alerts, plus the unglamorous work of finding what is running that nobody uses. Then the guardrails that stop the bill drifting back up once the attention moves elsewhere.
Paying more for cloud than you can explain?
Scope this with usAI workloads break several assumptions this practice is usually built on. They are expensive per request rather than per server, they need GPUs or a managed endpoint, and their failure mode is a plausible wrong answer rather than an error page.
Token spend attributed per feature and per customer, with budgets and alerts, because a runaway prompt loop is a cost incident and nothing in a standard setup will catch it.
A single path out to model providers, with caching, timeouts, retries and a defined fallback when a provider degrades, rather than API keys scattered across services.
Answer quality checked on every deploy alongside the tests. A prompt change is a production change, and it deserves the same gate.
Everything runs in your cloud tenancy from day one. Nothing is hosted on our side, so there is no migration to do when the engagement ends.
Documentation is a runbook someone on your team has actually followed, not a diagram nobody can execute from.
Managed services over clever ones, and the smallest architecture that meets the requirement. Novelty is a cost you pay every time somebody is on call.
Cost is treated as a requirement alongside latency and availability, not as a report someone reads with alarm a quarter later.
The questions that come up most on a first call about this practice, answered the way we would answer them on the phone.
Yes. Most of this work is inherited estates rather than empty accounts. We map what is running and what it costs before proposing a single change.